A High Frequency Wireless Power Transfer System for Electric Vehicle Charging Using Multi-layer Non-uniform Self-resonant Coil

Wireless EV (Electric Vehicle) charging is an emerging technology with rapid development in the past decade. Compared to wired EV chargers, wireless power transfer (WPT) enables safe and unobtrusive charging for EVs. This work proposes high frequency wireless charging using a self-resonant (SR) coil at several megahertz. A multi-layer self-resonant coil structure is proposed, allowing high quality factor coils to be fabricated from layers of inexpensive copper foil and dielectric film. Additionally, the self-resonant coil utilizes its interlayer capacitance for resonance, eliminating the external compensation capacitor and shrinking the overall volume of passive component to increase the power density. Comparing to other self-resonant coils in the literature, it exhibits the characteristics of achieving high quality factor and high inductance simultaneously. Prototype coils with 200 mm radius are fabricated and tested, achieving quality factor over 450 at 3 MHz. The fabricated air-core coil structure is low-cost and lightweight, with 200 mm radius, 3 mm thickness and only 2 oz copper traces. The power stages, including GaN (Gallium Nitride) transistor based inverter and SiC (Silicon Carbide) diode based rectifier, are designed with emphasis on reduction of PCB (Printed Circuit Board) layout parasitics. Experimental tests show 95.2% dc-dc efficiency with 6.6 kW power transferred across a 100 mm coil-to-coil distance. The power density is 52.5 kW/m2, without need for any external compensation components. This work validates the concept of high frequency compact WPT system for EV. Practical shielding design is proposed for the WPT system with self-resonant coils, considering the high frequency parallel resonance effect. Complete coil pads are fabricated and assembled, incorporating the ferrite cores, PTFE (Polytetrafluoroethylene) spacer, and aluminum plate. The system is validated with shielded SR coils, achieving 92.3% DC-DC efficiency and 7.1 kW/dm3 volumetric power density. This work demonstrates the first 6.6-kW WPT system using compact self-resonant coils with practical shielding implementation. The concept of proposed multi-layer self-resonant coil is extended to other possible structures. Different multi-layer self-resonant coil structures are compared and analyzed, giving design guidelines for their capabilities at different system operating frequencies

Open-Ended Multi-Modal Relational Reason for Video Question Answering

People with visual impairments urgently need helps, not only on the basic tasks such as guiding and retrieving objects , but on the advanced tasks like picturing the new environments. More than a guiding dog, they might want some devices which are able to provide linguistic interaction. Building on various research literature, we aim to conduct a research on the interaction between the robot agent and visual impaired people. The robot agent, applied VQA techniques, is able to analyze the environment, process and understand the pronouncing questions, and provide feedback to the human user. In this paper, we are going to discuss the related questions about this kind of interaction, the techniques we used in this work, and how we conduct our research

When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs

The use of machine learning (ML) models to assess and score textual data has become increasingly pervasive in an array of contexts including natural language processing, information retrieval, search and recommendation, and credibility assessment of online content. A significant disruption at the intersection of ML and text are text-generating large-language models such as generative pre-trained transformers (GPTs). We empirically assess the differences in how ML-based scoring models trained on human content assess the quality of content generated by humans versus GPTs. To do so, we propose an analysis framework that encompasses essay scoring ML-models, human and ML-generated essays, and a statistical model that parsimoniously considers the impact of type of respondent, prompt genre, and the ML model used for assessment model. A rich testbed is utilized that encompasses 18,460 human-generated and GPT-based essays. Results of our benchmark analysis reveal that transformer pretrained language models (PLMs) more accurately score human essay quality as compared to CNN/RNN and feature-based ML methods. Interestingly, we find that the transformer PLMs tend to score GPT-generated text 10-15\% higher on average, relative to human-authored documents. Conversely, traditional deep learning and feature-based ML models score human text considerably higher. Further analysis reveals that although the transformer PLMs are exclusively fine-tuned on human text, they more prominently attend to certain tokens appearing only in GPT-generated text, possibly due to familiarity/overlap in pre-training. Our framework and results have implications for text classification settings where automated scoring of text is likely to be disrupted by generative AI.Comment: Data available at: https://github.com/nd-hal/automated-ML-scoring-versus-generatio

REFINE: Reachability-based Trajectory Design using Robust Feedback Linearization and Zonotopes

Performing real-time receding horizon motion planning for autonomous vehicles while providing safety guarantees remains difficult. This is because existing methods to accurately predict ego vehicle behavior under a chosen controller use online numerical integration that requires a fine time discretization and thereby adversely affects real-time performance. To address this limitation, several recent papers have proposed to apply offline reachability analysis to conservatively predict the behavior of the ego vehicle. This reachable set can be constructed by utilizing a simplified model whose behavior is assumed a priori to conservatively bound the dynamics of a full-order model. However, guaranteeing that one satisfies this assumption is challenging. This paper proposes a framework named REFINE to overcome the limitations of these existing approaches. REFINE utilizes a parameterized robust controller that partially linearizes the vehicle dynamics even in the presence of modeling error. Zonotope-based reachability analysis is then performed on the closed-loop, full-order vehicle dynamics to compute the corresponding control-parameterized, over-approximate Forward Reachable Sets (FRS). Because reachability analysis is applied to the full-order model, the potential conservativeness introduced by using a simplified model is avoided. The pre-computed, control-parameterized FRS is then used online in an optimization framework to ensure safety. The proposed method is compared to several state of the art methods during a simulation-based evaluation on a full-size vehicle model and is evaluated on a 1/10th race car robot in real hardware testing. In contrast to existing methods, REFINE is shown to enable the vehicle to safely navigate itself through complex environments

AMOM: Adaptive Masking over Masking for Conditional Masked Language Model

Transformer-based autoregressive (AR) methods have achieved appealing performance for varied sequence-to-sequence generation tasks, e.g., neural machine translation, summarization, and code generation, but suffer from low inference efficiency. To speed up the inference stage, many non-autoregressive (NAR) strategies have been proposed in the past few years. Among them, the conditional masked language model (CMLM) is one of the most versatile frameworks, as it can support many different sequence generation scenarios and achieve very competitive performance on these tasks. In this paper, we further introduce a simple yet effective adaptive masking over masking strategy to enhance the refinement capability of the decoder and make the encoder optimization easier. Experiments on 3 different tasks (neural machine translation, summarization, and code generation) with 15 datasets in total confirm that our proposed simple method achieves significant performance improvement over the strong CMLM model. Surprisingly, our proposed model yields state-of-the-art performance on neural machine translation (34.62 BLEU on WMT16 EN to RO, 34.82 BLEU on WMT16 RO to EN, and 34.84 BLEU on IWSLT De to En) and even better performance than the AR Transformer on 7 benchmark datasets with at least 2.2x speedup. Our code is available at GitHub

Integrative Analysis of the lncRNA and mRNA Transcriptome Revealed Genes and Pathways Potentially Involved in the Anther Abortion of Cotton (Gossypium hirsutum L.)

Cotton plays an important role in the economy of many countries. Many studies have revealed that numerous genes and various metabolic pathways are involved in anther development. In this research, we studied the differently expressed mRNA and lncRNA during the anther development of cotton between the cytoplasmic male sterility (CMS) line, C2P5A, and the maintainer line, C2P5B, using RNA-seq analysis. We identified 17,897 known differentially expressed (DE) mRNAs, and 865 DE long noncoding RNAs (lncRNAs) that corresponded to 1172 cis-target genes at three stages of anther development using gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of DE mRNAs; and cis-target genes of DE lncRNAs probably involved in the degradation of tapetum cells, microspore development, pollen development, and in the differentiation, proliferation, and apoptosis of the anther cell wall in cotton. Of these DE genes, LTCONS_00105434, LTCONS_00004262, LTCONS_00126105, LTCONS_00085561, and LTCONS_00085561, correspond to cis-target genes Ghir_A09G011050.1, Ghir_A01G005150.1, Ghir_D05G003710.2, Ghir_A03G016640.1, and Ghir_A12G005100.1, respectively. They participate in oxidative phosphorylation, flavonoid biosynthesis, pentose and glucuronate interconversions, fatty acid biosynthesis, and MAPK signaling pathway in plants, respectively. In summary, the transcriptomic data indicated that DE lncRNAs and DE mRNAs were related to the anther development of cotton at the pollen mother cell stage, tetrad stage, and microspore stage, and abnormal expression could lead to anther abortion, resulting in male sterility of cotton